from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2022-12-14 14:02:32.761199
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64('2020-12-27'),
'orange', 'inside top left'),
'Weihnachten 2020': (np.datetime64('2020-12-24'), np.datetime64('2020-12-27'),
'blue', 'inside top left'),
'3. Lockdown': (np.datetime64('2020-12-27'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Wed, 14, Dec, 2022
Time: 14:02:39
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -51.2141
Nobs: 870.000 HQIC: -51.5186
Log likelihood: 11472.4 FPE: 3.49759e-23
AIC: -51.7074 Det(Omega_mle): 3.15572e-23
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.295415 0.049864 5.924 0.000
L1.Burgenland 0.105851 0.034120 3.102 0.002
L1.Kärnten -0.106889 0.018316 -5.836 0.000
L1.Niederösterreich 0.214668 0.071618 2.997 0.003
L1.Oberösterreich 0.088900 0.067863 1.310 0.190
L1.Salzburg 0.249775 0.036220 6.896 0.000
L1.Steiermark 0.029891 0.047569 0.628 0.530
L1.Tirol 0.127301 0.038742 3.286 0.001
L1.Vorarlberg -0.062135 0.033271 -1.868 0.062
L1.Wien 0.061466 0.060679 1.013 0.311
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.064123 0.102607 0.625 0.532
L1.Burgenland -0.009560 0.070210 -0.136 0.892
L1.Kärnten 0.049185 0.037688 1.305 0.192
L1.Niederösterreich -0.173861 0.147371 -1.180 0.238
L1.Oberösterreich 0.364925 0.139643 2.613 0.009
L1.Salzburg 0.286318 0.074532 3.842 0.000
L1.Steiermark 0.108426 0.097885 1.108 0.268
L1.Tirol 0.318442 0.079721 3.994 0.000
L1.Vorarlberg 0.024203 0.068462 0.354 0.724
L1.Wien -0.026128 0.124861 -0.209 0.834
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.199256 0.025815 7.718 0.000
L1.Burgenland 0.090141 0.017665 5.103 0.000
L1.Kärnten -0.009090 0.009482 -0.959 0.338
L1.Niederösterreich 0.267353 0.037078 7.211 0.000
L1.Oberösterreich 0.114125 0.035134 3.248 0.001
L1.Salzburg 0.052748 0.018752 2.813 0.005
L1.Steiermark 0.015680 0.024627 0.637 0.524
L1.Tirol 0.101597 0.020058 5.065 0.000
L1.Vorarlberg 0.056327 0.017225 3.270 0.001
L1.Wien 0.112930 0.031414 3.595 0.000
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.105111 0.026494 3.967 0.000
L1.Burgenland 0.047747 0.018129 2.634 0.008
L1.Kärnten -0.016971 0.009732 -1.744 0.081
L1.Niederösterreich 0.197044 0.038052 5.178 0.000
L1.Oberösterreich 0.278640 0.036057 7.728 0.000
L1.Salzburg 0.118111 0.019245 6.137 0.000
L1.Steiermark 0.100192 0.025275 3.964 0.000
L1.Tirol 0.126622 0.020585 6.151 0.000
L1.Vorarlberg 0.069713 0.017678 3.944 0.000
L1.Wien -0.027006 0.032240 -0.838 0.402
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.131586 0.047896 2.747 0.006
L1.Burgenland -0.053888 0.032774 -1.644 0.100
L1.Kärnten -0.037197 0.017593 -2.114 0.034
L1.Niederösterreich 0.167094 0.068792 2.429 0.015
L1.Oberösterreich 0.132815 0.065184 2.038 0.042
L1.Salzburg 0.291040 0.034791 8.365 0.000
L1.Steiermark 0.034112 0.045692 0.747 0.455
L1.Tirol 0.162519 0.037213 4.367 0.000
L1.Vorarlberg 0.107785 0.031958 3.373 0.001
L1.Wien 0.065731 0.058284 1.128 0.259
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.059802 0.037948 1.576 0.115
L1.Burgenland 0.038421 0.025967 1.480 0.139
L1.Kärnten 0.049834 0.013939 3.575 0.000
L1.Niederösterreich 0.227475 0.054504 4.174 0.000
L1.Oberösterreich 0.271488 0.051646 5.257 0.000
L1.Salzburg 0.058871 0.027565 2.136 0.033
L1.Steiermark -0.007176 0.036202 -0.198 0.843
L1.Tirol 0.157528 0.029484 5.343 0.000
L1.Vorarlberg 0.068932 0.025320 2.722 0.006
L1.Wien 0.075490 0.046179 1.635 0.102
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.185239 0.045484 4.073 0.000
L1.Burgenland 0.018912 0.031123 0.608 0.543
L1.Kärnten -0.060340 0.016707 -3.612 0.000
L1.Niederösterreich -0.093961 0.065327 -1.438 0.150
L1.Oberösterreich 0.178119 0.061902 2.877 0.004
L1.Salzburg 0.060819 0.033039 1.841 0.066
L1.Steiermark 0.229489 0.043391 5.289 0.000
L1.Tirol 0.488683 0.035339 13.828 0.000
L1.Vorarlberg 0.050393 0.030348 1.660 0.097
L1.Wien -0.055458 0.055349 -1.002 0.316
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.157847 0.051714 3.052 0.002
L1.Burgenland 0.000098 0.035386 0.003 0.998
L1.Kärnten 0.066428 0.018995 3.497 0.000
L1.Niederösterreich 0.200636 0.074275 2.701 0.007
L1.Oberösterreich -0.069248 0.070381 -0.984 0.325
L1.Salzburg 0.220178 0.037564 5.861 0.000
L1.Steiermark 0.112676 0.049334 2.284 0.022
L1.Tirol 0.083743 0.040180 2.084 0.037
L1.Vorarlberg 0.123618 0.034505 3.583 0.000
L1.Wien 0.105815 0.062930 1.681 0.093
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.358320 0.030531 11.736 0.000
L1.Burgenland 0.006390 0.020891 0.306 0.760
L1.Kärnten -0.025209 0.011214 -2.248 0.025
L1.Niederösterreich 0.229191 0.043851 5.227 0.000
L1.Oberösterreich 0.155598 0.041551 3.745 0.000
L1.Salzburg 0.052918 0.022177 2.386 0.017
L1.Steiermark -0.016425 0.029126 -0.564 0.573
L1.Tirol 0.121309 0.023721 5.114 0.000
L1.Vorarlberg 0.071446 0.020371 3.507 0.000
L1.Wien 0.047883 0.037153 1.289 0.197
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.038003 0.158370 0.180450 0.167735 0.140949 0.125581 0.064896 0.219539
Kärnten 0.038003 1.000000 0.000605 0.131388 0.026606 0.098886 0.432503 -0.049494 0.101647
Niederösterreich 0.158370 0.000605 1.000000 0.344885 0.169435 0.311659 0.125739 0.191060 0.341757
Oberösterreich 0.180450 0.131388 0.344885 1.000000 0.233848 0.341302 0.176717 0.180084 0.272875
Salzburg 0.167735 0.026606 0.169435 0.233848 1.000000 0.152745 0.136249 0.152385 0.141525
Steiermark 0.140949 0.098886 0.311659 0.341302 0.152745 1.000000 0.158738 0.147602 0.094128
Tirol 0.125581 0.432503 0.125739 0.176717 0.136249 0.158738 1.000000 0.122326 0.166150
Vorarlberg 0.064896 -0.049494 0.191060 0.180084 0.152385 0.147602 0.122326 1.000000 0.019411
Wien 0.219539 0.101647 0.341757 0.272875 0.141525 0.094128 0.166150 0.019411 1.000000